A neural network can be thought of as a system that mimics the structure of the human brain. The neural network may be divided into layers, and each layer may include a plurality of nodes. Each node may function similarly to a neuron. In examples, a particular input value may be applied to any number of the nodes of the network. Each input value of each node is given a weight, and each node generates an output value that is a function of the sum of its weighted input values. The weight that is assigned to a particular input value is determined by a data transfer function, which may be constant or may vary over time. Moreover, various multiplication and accumulation functions may be used in the neural network to converge on a solution.
The same numbers are used throughout the disclosure and the figures to reference like components and features. Numbers in the 100 series refer to features originally found in FIG. 1; numbers in the 200 series refer to features originally found in FIG. 2; and so on.